Deep Electrification and Renewable Energy in a Remote Canadian Community
Bibliographic record
Abstract
This study examines high-penetration of renewable energy options for Fort Chipewyan (an off-grid community in northern Alberta, Canada). This analysis goes beyond modelling hybrid diesel-renewable electricity supply, to also consider deep electrification scenarios that not only aim to electrify the community’s heating and transportation energy demands, which can almost triple the average 35 MWh/day electricity load in a highly seasonal manner. HOMER Pro software was used to create seven different electricity use scenarios, and the outcomes were compared to optimize hybrid renewable energy technologies including solar PV, wind turbines, batteries, and hydrogen fuel cells to meet forecast electricity demand. Sensitivity analyses were conducted to verify the effects of factors such as solar radiation, wind speed, the capital cost of solar PV and wind turbines, diesel prices, and CO2 penalty cost on the cost of electricity (COE). While the community has already installed 2.6 MW of solar PV in 2019, this research found that wind energy offers a low cost long-term renewable energy option if deep electrification goals are pursued due to the solar resource being out of sync with winter heating demands. Without heat and transportation electrification, a wind-diesel-storage system could reduce the COE by 10% (from 0.326 $/kWh to 0.295 $/kWh), while reducing CO2e emissions by 12% (3000 tCO2e) annually compared to the existing system. Additionally, adding batteries along with solar PV and wind turbines cuts annual diesel fuel costs by $1.6 million. The findings also show that if transportation is electrified, a PV-wind-battery-diesel system can reduce CO2 emissions by almost 16,500 tCO2e annually with a resulting electricity cost of 0.291 $/kWh. Efforts to fully decarbonize the energy system however become increasingly expensive, ranging from 3 to 6 times the current energy cost for deep decarbonization and electrification, largely due to the overbuild requirements for variable renewable energy technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".